When does DBSCAN beat k-means, and how do you evaluate clusters with no labels?

k-means assumes round, equal-size blobs and a known k. DBSCAN finds arbitrary shapes and outliers but has its own knobs. The hard part is judging clusters without labels. Here is the comparison and the evaluation toolkit.

Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.